Hydrol. Earth Syst. Sci., 20, 803–822, 2016
www.hydrol-earth-syst-sci.net/20/803/2016/
doi:10.5194/hess-20-803-2016
© Author(s) 2016. CC Attribution 3.0 License.
The WACMOS-ET project – Part 1: Tower-scale evaluation
of four remote-sensing-based evapotranspiration algorithms
D. Michel1 , C. Jiménez2,3 , D. G. Miralles4,5 , M. Jung6 , M. Hirschi1 , A. Ershadi7 , B. Martens5 , M. F. McCabe7 ,
J. B. Fisher8 , Q. Mu9 , S. I. Seneviratne1 , E. F. Wood10 , and D. Fernández-Prieto11
1 Institute
for Atmospheric and Climate Science, ETH Zürich, Zürich, Switzerland
Paris, France
3 LERMA, Paris Observatory, Paris, France
4 Department of Earth Sciences, VU University Amsterdam, Amsterdam, the Netherlands
5 Laboratory of Hydrology and Water Management, Ghent University, Ghent, Belgium
6 Max Planck Institute for Biogeochemistry, Jena, Germany
7 Division of Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology,
Thuwal, Saudi Arabia
8 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
9 Department of Ecosystem and Conservation Sciences, University of Montana, Missoula, Montana, USA
10 Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USA
11 ESRIN, European Space Agency, Frascati, Italy
2 Estellus,
Correspondence to: D. Michel ([email protected])
Received: 2 October 2015 – Published in Hydrol. Earth Syst. Sci. Discuss.: 20 October 2015
Accepted: 2 February 2016 – Published: 23 February 2016
Abstract. The WAter Cycle Multi-mission Observation
Strategy – EvapoTranspiration (WACMOS-ET) project has
compiled a forcing data set covering the period 2005–2007
that aims to maximize the exploitation of European Earth
Observations data sets for evapotranspiration (ET) estimation. The data set was used to run four established ET algorithms: the Priestley–Taylor Jet Propulsion Laboratory model
(PT-JPL), the Penman–Monteith algorithm from the MODerate resolution Imaging Spectroradiometer (MODIS) evaporation product (PM-MOD), the Surface Energy Balance System (SEBS) and the Global Land Evaporation Amsterdam
Model (GLEAM). In addition, in situ meteorological data
from 24 FLUXNET towers were used to force the models,
with results from both forcing sets compared to tower-based
flux observations. Model performance was assessed on several timescales using both sub-daily and daily forcings. The
PT-JPL model and GLEAM provide the best performance
for both satellite- and tower-based forcing as well as for the
considered temporal resolutions. Simulations using the PMMOD were mostly underestimated, while the SEBS performance was characterized by a systematic overestimation. In
general, all four algorithms produce the best results in wet
and moderately wet climate regimes. In dry regimes, the correlation and the absolute agreement with the reference tower
ET observations were consistently lower. While ET derived
with in situ forcing data agrees best with the tower measurements (R 2 = 0.67), the agreement of the satellite-based
ET estimates is only marginally lower (R 2 = 0.58). Results
also show similar model performance at daily and sub-daily
(3-hourly) resolutions. Overall, our validation experiments
against in situ measurements indicate that there is no single best-performing algorithm across all biome and forcing
types. An extension of the evaluation to a larger selection of
85 towers (model inputs resampled to a common grid to facilitate global estimates) confirmed the original findings.
1
Introduction
Research on climate variability and the development of predictive capabilities relies largely on globally available reference data time series of the various components of the energy
Published by Copernicus Publications on behalf of the European Geosciences Union.
804
and water cycles. Turbulent fluxes of sensible and latent heat
determine the development of the planetary boundary layer
and thus govern the interactions between the Earth surface
and the atmosphere. Evapotranspiration (ET) represents the
time-integrated flux of latent heat and is an essential component of the energy and water cycle, playing a key role in
meteorology and climate as well as agriculture (see, e.g., Ershadi et al., 2014).
Historically, there has been a lack of reliable estimates of
turbulent fluxes, since the partitioning of the available energy
at the Earth’s surface into these fluxes is complex and characterized by large spatial and temporal variability. Also, these
components of the energy balance cannot be monitored directly on a global scale by remote sensing techniques. Thus,
efforts to produce satellite-based estimates typically involve
combining multi-sensor data sets with predictive formulations of varying complexity, ranging from relatively simple empirical formulations to more complex modeling approaches (see, e.g., Courault et al. (2005), Kalma et al. (2008)
and Wang and Dickinson (2012) for comprehensive reviews).
In recent years, such efforts have generated global ET products (Mu et al., 2007; Fisher et al., 2008; Jung et al., 2010;
Zhang et al., 2010; Vinukollu et al., 2011; Miralles et al.,
2011a) that have typically been evaluated by comparing them
individually to in situ data or by inter-comparing them with
other existing global heat flux estimates. For example, within
the Evaluation and inter-comparison of existing land evapotranspiration products (Landflux-Eval initiative) initiative of
the Global Energy and Water cycle Exchanges (GEWEX)
Data and Assessments Panels (GDAP), Jiménez et al. (2011)
investigated 3 years (1993–1995) of global sensible and
latent fluxes from a selection of 12 products, including
satellite-based estimates, atmospheric reanalyses and offline
land surface model simulations, while Mueller et al. (2011)
examined a total of 30 observation-based ET estimates from
similar sources over the longer period of 1989–1995, while
also providing a comparison with global climate model simulations contributing to the Intergovernmental Panel on Climate Change (IPCC) fourth assessment report. More recently, Mueller et al. (2013) extended the previous LandFluxEVAL evaluations and presented two monthly global ET
synthesis products, merged from individual data sets spanning the periods 1989–1995 and 1989–2005. Based on the
Mueller et al. (2013) methodology, Mao et al. (2015) synthesized a global ET time series for the period 1982–2010
based on a set of diagnostic ET products (including data sets
produced with PT-JPL (Priestley–Taylor Jet Propulsion Laboratory model) and GLEAM (Global Land Evaporation Amsterdam Model)) to investigate the role of anthropogenic and
climatic controls on ET trends. For the period 1982–2013
Zhang et al. (2015) produced a satellite-based global ET data
set using remote sensing data and daily surface meteorology records for the investigation of multidecadal changes in
ET, which was validated against precipitation and discharge
records.
Hydrol. Earth Syst. Sci., 20, 803–822, 2016
D. Michel et al.: The WACMOS-ET project – Part 1
The GEWEX-LandFlux initiative is currently working towards producing an observation-based data set of heat fluxes
that can be used together with related GDAP products to enable a joint analysis of the water and energy cycles (Jimenez
et al., 2012). To contribute towards that goal, the European Space Agency (ESA) has conducted the Water Cycle Multi-mission Observation Strategy EvapoTranspiration
project (WACMOS-ET), aimed at the identification of appropriate algorithms to develop regional and global ET products.
WACMOS-ET efforts, which aimed at maximizing the use
of European Earth Observation assets, have also included the
compilation of a multi-sensor data set to run the ET methodologies for a 3-year period (2005-2007).
In WACMOS-ET, the methodologies by Su (2002) (Surface Energy Balance System, hereafter referred to as
SEBS), Mu et al. (2011) (Penman–Monteith algorithm
from the MODerate resolution Imaging Spectroradiometer (MODIS) evaporation product, PM-MOD), Fisher et al.
(2008) (Priestley–Taylor Jet Propulsion Laboratory model,
PT-JPL) and Miralles et al. (2011a, b) (Global Land Evaporation Amsterdam Model, GLEAM) were selected to produce ET estimates on different temporal and spatial scales.
The same algorithms have also been examined at a selection
of different tower sites in a recent paper by McCabe et al.
(2015) in preparation for the GEWEX-LandFlux product. In
McCabe et al. (2015) the algorithms are run at 3-hourly time
steps with both point-scale inputs (from tower meteorological observations) and gridded inputs (from the GEWEXLandFlux global forcing data set) over a longer time period. Here, the ET algorithms are run with the WACMOSET forcings (see Sect. 2.2) and the analyses of model performance are extended to evaluate different timescales (3-hourly
and daily) and time integrations (nighttime, daytime and full
day). In a companion paper, Miralles et al. (2016) present the
second part of the WACMOS-ET study, in which PT-JPL,
GLEAM and PM-MOD are evaluated on the global scale.
The paper is structured as follows. First, the WACMOSET input data set is described in detail, together with the
tower flux data used for driving and evaluating the ET
models. This is followed by an evaluation of ET model
performance on the tower scale using the tower eddycovariance (EC) fluxes as the reference data set. The model
evaluation is first performed over a selection of 24 stations
covering nine biomes in three continents (Europe, North
America and Australia), in which models are run based on
in situ and remote sensing forcing. Then the validation is extended to embrace a larger sample of 85 towers, in which
models are driven only by satellite data resampled to a common grid. Finally, the main findings of our model evaluation
on the pixel scale are summarized.
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2
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Methods and data
u=
Here the ET methodologies comprising the WACMOS-ET
effort together with the input data sets that have been compiled to run the models and evaluate the ET estimates are
presented. A summary of the data sets and the model-specific
forcing requirements is provided in Table 1.
2.1
ET models
The four algorithms selected here estimate the fraction of
the available energy at the surface used by the soil and
canopy evaporation processes. Therefore, the available energy (i.e., the difference between the surface net radiation
and the ground heat flux) is a key input for all algorithms.
However, this evaporative fraction is parameterized differently by each model. SEBS is an energy balance model (Su,
2002) based on a detailed parameterization of the sensible
heat flux at the surface, where ET is estimated as the residual
of the surface energy balance once the sensible heat flux is
calculated. Therefore, key inputs are the surface temperature
and the temperature gradient between the surface and the air,
and a key component of the model is the aerodynamic resistance to sensible heat transfer. PM-MOD (Mu et al., 2011)
derives ET directly based on the Penman–Monteith equation (Monteith, 1965), which relates the latent heat flux to
the vapor pressure deficit between the surface and the overlying air and requires a resistance parameter to characterize
the canopy transpiration. PT-JPL (Fisher et al., 2008) and
GLEAM (Miralles et al., 2011a) are based on first determining the potential evaporation by applying the Priestley
and Taylor equation (Priestley and Taylor, 1972), followed
by reducing the potential evaporation to actual evaporation
with a number of evaporative stress factors. The derivation
of these stress factors is different between both models. PTJPL requires the vapor pressure deficit, relative humidity and
visible-infrared-related vegetation indexes, while GLEAM
combines microwave data of vegetation optical depth and
soil moisture. A more detailed description of the input requirements for each model can be found in Table 1, while a
more comprehensive description of each individual model is
given in the following sections.
2.1.1
SEBS
SEBS (Su, 2002) is a one-source energy balance algorithm
that is arguably one of the most widely used energy balance
approaches to derive turbulent fluxes. The SEBS model calculates the sensible heat flux (H ) based on the Monin and
Obukhov theory (Monin and Obukhov, 1954):
θ0 − θa =
z z − d0
z − d0
H
0h
ln
− 0h
+ 0h
,
ku∗ ρcp
z0h
L
L
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(1)
z u∗
z − d0
z − d0
0h
ln
− 0m
+ 0m
,
k
z0m
L
L
L=−
ρcp u3∗ θv
,
kgH
(2)
(3)
where u is the wind speed, u∗ is the friction velocity, k is
the von Kármán constant, z is the height above the surface,
d0 is the zero plane displacement height, z0m and z0h are
the roughness heights for momentum and heat transfer, and
0m and 0h are the stability correction functions for momentum and sensible heat transfer, respectively. L refers to the
Obukhov length, ρ is the air density, θ0 is potential land
surface temperature, θa is the potential air temperature at
height z, g is the gravity acceleration and θv is the potential virtual air temperature at level z. When the suitable data
are available, the only unknowns are H , u∗ and L. This allows the calculation of H and the further estimation of ET
based on closing the energy balance at the surface, i.e., ET
is estimated as the difference between net radiation (Rn ) and
the sum of the calculated H and ground flux (G).
Additionally, in order to constrain H estimates, two limiting cases are considered that set upper and lower bounds for
the evaporative fraction. Under very dry conditions, ET becomes 0 and H is at its maximum, set by Rn − G. Under wet
conditions, ET occurs at potential rates and therefore H is at
its minimum. In this wet case, H is calculated via reverse application of the Penman–Monteith equation (see Sect. 2.1.2)
assuming that the surface resistance is zero.
SEBS has been extensively validated against tower measurements and has proved to estimate realistic evaporation
rates on a variety of scales, ranging from local to regional (Jia
et al., 2003; Su et al., 2005; McCabe and Wood, 2006). As an
example, Chen et al. (2015) recently reported an average correlation of ∼ 0.8 and root mean square difference (RMSD)
of 0.7 mm day−1 against eddy-covariance measurements in a
validation of monthly SEBS ET aggregates over China.
As a one-source energy model, SEBS does not separate
the contributing components of ET (i.e., transpiration, interception loss, bare-soil evaporation), unlike the other models
studied in WACMOS-ET, which provide independent estimates of these vapor sources. Although not examined here,
we note that two-source energy balance models can also treat
the soil and vegetation components separately (e.g., Kustas
and Norman, 2000; Anderson et al., 2007) but have had limited application on the global scale.
2.1.2
PM-MOD
PM-MOD (Mu et al., 2011) is based on the Penman–
Monteith equation. It estimates ET as the sum of interception loss (I ), transpiration (ETt ) and evaporation from the
soil (ETs ). The interception loss is modeled as
Hydrol. Earth Syst. Sci., 20, 803–822, 2016
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D. Michel et al.: The WACMOS-ET project – Part 1
Table 1. Table summarizing the model inputs. Listed are the main inputs, product selected, original temporal and spatial resolutions, and the
satellite sensors used to derive the product.
Variable
Models
Product
Resolution
Sensors
Surface radiation
All
SRB
3-hourly, 100 km
Several VIS-IR sensors
Surface temperature
polar
geostationarity
SEBS
IPMA
twice daily, 1 km
hourly, 5 km
AATSR
MSG-2, MT-SAT, GOES-12
ERA-Interim
3-hourly, 75 km
Assimilation of
satellite and
other meteo
observations
Surface meteorology
temperature
humidity
wind
All
SEBS, PM-MOD, PT-JPL
SEBS
f APAR, LAI
SEBS, PM-MOD, PT-JPL
From ESA
GlobAlbedo
8 days, 1 km
VEGETATION, MERIS, MODIS
Soil moisture
GLEAM
ESA-CCI
daily, 25 km
SSM/I, TMI, AMSR-E, ASCAT
Precipitation
GLEAM
CMORPH
30 min, 15 km
AMSU-B, AMSR-E, TMI
Snow water
GLEAM
ESA
daily, 1 km
AMSR-E
f APAR: fraction of absorbed photosynthetically active radiation; VIS-IR:visible and infrared; IPMA: Portuguese Sea and Atmosphere Institute; AATSR: Advanced
Along-Track Scanning Radiometer; MSG-2: Meteosat Second Generation 2; MT-SAT: Multi-functional Transport Satellites; GOES-12: Geostationary Operational
Environmental Satellite 12; VEGETATION: SPOT (Satellite Pour l’Observation de la Terre) vegetation; MERIS: Medium Resolution Imaging Spectrometer;
SSM/I: Special Sensor Microwave Imager; TMI: TRMM (Tropical Rainfall Measuring Mission) Microwave Imager; AMSR-E: Advanced Microwave Scanning
Radiometer – Earth Observing System; ASCAT: Advanced Scatterometer; AMSU-B: Advanced Microwave Sounding Unit-B.
I = fwet fc
1 (Rn − G) + ρcp VPD/rawc
,
r wc
λ 1 + γ rswc
(4)
a
where 1 is the slope of the curve relating saturated water
vapor pressure to temperature, VPD is the vapor pressure
deficit, γ is the psychrometric constant, fc is the canopy fraction, fwet is the wet cover fraction based on the derivation by
Fisher et al. (2008) (see Eq. (9) in Sect. 2.1.3), and rawc and
rswc are aerodynamic and surface resistances against evaporation of intercepted water (calculated as a function of air
temperature and leaf area index, LAI).
Canopy transpiration is estimated as
1 (Rn − G) + ρcp VPD/rat
ETt = (1 − fwet ) fc
,
rt
λ 1 + γ rst
a
2.1.3
PT-JPL
PT-JPL (Fisher et al., 2008) is based upon the Priestley and
Taylor equation. As in PM-MOD, ET is estimated as the sum
of interception loss I , transpiration ETt and evaporation from
the soil ETs . The driving equations in the model are
1
Rc ,
1+γ n
1
ETs = fwet + fsm (1 − fwet ) α
Rns − G ,
1+γ
1
Rc ,
I = fwet α
1+γ n
ETt = (1 − fwet ) fg fT fM α
(5)
where rat and rst are the aerodynamic and surface resistances
against transpiration. rat is determined in a similar way to rawc ,
and rst is a function of stomatal conductance, biome-constant
values of cuticular conductance and canopy boundary-layer
conductance. The values of stomatal conductance are a function of air temperature, VPD and LAI.
Evaporation from the soil surface is the sum of evaporation from wet soil and evaporation from saturated soil, which
are both calculated separately based on Eq. (7) with specific
values of aerodynamic and surface resistances for bare soils
and a soil moisture constraint (fsm ) depending on relative
humidity (taken from Fisher et al. (2008), see Sect. 2.1.3).
Hydrol. Earth Syst. Sci., 20, 803–822, 2016
The Mu et al. (2011) daily ET estimates have
been previously validated against EC measurements from
46 FLUXNET towers in North America, reporting for the
daily estimates an average RMSD of ∼ 0.9 mm day−1 and a
∼ 0.6 average correlation coefficient.
(6)
(7)
(8)
where α is known as the Priestley and Taylor (PT) coefficient
and is considered here as a constant value (1.26) (Priestley
and Taylor, 1972) that aims to summarize the atmospheric
term in the Penman–Monteith equation (Eq. 5), λ is the latent heat of vaporization and fwet , fg , fM , fsm and fT are
ecophysiological constraint functions with values between 0
and 1 referred to as f functions. The f functions are given
by
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D. Michel et al.: The WACMOS-ET project – Part 1
fwet = RH4 ,
807
(9)
fg = f APAR/f IPAR,
(10)
fM = f APAR/f APARmax ,
(11)
VPD
fsm = RH
fT = e
−
(12)
,
s −Topt 2
Topt
T
,
(13)
where fwet is the relative surface wetness, fg is green canopy
fraction, f APAR (f IPAR) is the fraction of absorbed (intercepted) photosynthetically active radiation, fM is a plant
moisture constraint, f APARmax is the maximum of f APAR,
fsm is a soil moisture constraint, fT is a plant temperature
constraint and Topt is the optimum plant growth temperature,
estimated as the air temperature at the time of peak canopy
activity when the highest f APAR and minimum VPD occur. Note that as the input data set does not include f IPAR;
f IPAR is derived from the rescaled project LAI by inverting
the model original relationships between LAI and f IPAR.
Using this methodology, monthly estimates of ET were
tested against EC measurements from 16 FLUXNET towers
worldwide (Baldocchi et al., 2001) with a reported average
RMSD of ∼ 0.4 mm day−1 and a ∼ 0.9 average correlation
coefficient (Fisher et al., 2008). Note that unlike the above
statistics reported for SEBS and PM-MOD, these numbers
come from the model run with the tower meteorology, instead of global forcings.
2.1.4
GLEAM (Miralles et al., 2011a, b) calculates ET via the PT
equation, a soil moisture stress computation and a Gash analytical model of rainfall interception loss (Gash, 1979). In
the absence of snow, evaporation from land is calculated as
(14)
where ETtc is transpiration from tall canopy, ETsc is transpiration from short vegetation, ETs is soil evaporation and I is
tall canopy interception loss. β is a constant used to account
for the times at which vegetation is wet; thus, transpiration
occurs at lower rates (β = 0.93) (Gash and Stewart, 1977).
The first three terms in Eq. (14) are derived using the
Priestley and Taylor equation, so ET becomes
ET =
1 ftc Stc αtc Rntc − Gtc + fsc Ssc αsc Rnsc − Gsc + fs Ss αs Rns − Gs
+ βI,,
λ(1 + γ )
(15)
where the subscripts “tc”, “sc” and “s” correspond to tall
vegetation, short vegetation and bare soil (respectively) and
the fraction of each of these three cover types per pixel is
represented by f . Different cover types have different values of α and parameterizations of G; additionally, Rn is distributed within the cover fractions using average albedo ratios from the literature. S represents the evaporative stress
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2.2
2.2.1
GLEAM
ET = ETtc + ETsc + ETs + βI,
due to soil moisture deficit and vegetation phenology. Soil
moisture deficit is estimated using a multilayer running water
balance to describe the infiltration of observed precipitation
through the vertical soil profile. Microwave soil moisture observations are assimilated into the soil profile (Martens et al.,
2016). In vegetated land covers, phenology effects on ET are
based on microwave observations of vegetation optical depth,
used as a proxy of vegetation water content.
I is independently derived using a Gash analytical model
(Gash, 1979), in which a running water balance for canopies
and trunks is driven by observations of precipitation. The
derivation of the parameters and the global implementation
and validation of this I model are described in Miralles et al.
(2010). For regions covered by ice and snow, sublimation is
calculated based on a PT equation parameterized for ice and
supercooled waters (Murphy and Koop, 2005).
The ET estimates from GLEAM have been validated
against eddy covariance towers worldwide; reported average correlations are 0.83 and 0.90 for daily and
monthly estimates, respectively, with an average RMSD of
∼ 0.3 mm day−1 , based on a sample of 43 towers (Miralles
et al., 2011a), and correlations of 0.71–0.75 and 0.81–0.86
for daily and monthly estimates, respectively, based on a
sample of 163 towers and different satellite products as forcing (Miralles et al., 2016).
Model inputs
Surface meteorology
The European Centre for Medium-range Weather Forecasts (ECMWF) Re-Analysis-Interim (ERA-Interim) (Dee
et al., 2011) was selected to provide the near-surface meteorology every 3 h at a spatial resolution of ∼ 75 km. The
use of reanalysis data is necessary as satellite observations
are generally unable to retrieve the surface variables needed,
such as temperature, humidity and wind speed, with sufficient accuracy or at a suitable sub-daily temporal resolution.
Although products of near-surface air temperature and humidity derived from satellite sounders exist (Ferguson and
Wood, 2010), atmospheric reanalyses have the advantage of
providing regular sub-daily estimates for all weather conditions. ERA-Interim is also used in the derivation of the land
surface temperature product (see Sect. 2.2.2), to ensure interproduct consistency between air and surface temperatures.
In terms of accuracy, ERA-Interim data have been evaluated through comparison with other reanalyses and weather
stations over specific areas, showing a good general performance (e.g., Mooney et al., 2011; Szczypta et al., 2011).
2.2.2
Land surface temperature
Land surface temperature (LST) estimates have been internally generated by the project from level 1 radiances from
the Advanced Along-Track Scanning Radiometer (AATSR)
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D. Michel et al.: The WACMOS-ET project – Part 1
onboard ESA’s Environmental Satellite (EnviSat) polarorbiting satellite, from Multi-functional Transport Satellites (MT-SAT) 2 (over Australia), Meteosat Second Generation (MSG) 2 and Geostationary Operational Environmental Satellite (GOES) 12. The data sets are provided over a
sinusoidal grid with 1 km resolution for AATSR at the two
satellite overpasses per day (∼ 10:00 LT descending node)
and 5 km for the remaining sensors (1-hourly estimates for
MSG and MT-SAT, 3-hourly for GOES). Ancillary atmospheric information for the inversion of the L1 radiances
comes from ERA-Interim. Estimates of surface emissivity
are taken from the Global Infrared Land Surface Emissivity
UW-Madison Baseline Fit Emissivity Database developed by
Seemann et al. (2008).
2.2.3
Surface radiation
The National Aeronautics and Space Administration (NASA)–GEWEX Surface Radiation Budget (SRB)
satellite product version 3.1 (Stackhouse et al., 2004) is used
to provide the surface net radiation input to the ET models.
The SRB product is used by a large number of global
ET algorithms to characterize the radiation at the surface,
given its relatively long data record and sub-daily temporal
resolution. SRB data sets include global 3-hourly averages
of surface and top-of-atmosphere longwave and shortwave
radiative parameters on a ∼ 100 km grid.
2.2.4
LAI and f APAR
To characterize the vegetation state using visible and nearinfrared wavebands, estimates of LAI and f APAR have
been derived by applying the Joint Research Centre (JRC)
two-stream inversion package (hereafter TIP) (Pinty et al.,
2007, 2011a, b) to the ESA GlobAlbedo bihemispherical reflectances. Here, LAI is defined as the one-sided leaf area
per unit ground area and f APAR as the fraction of absorbed
photosynthetically active radiation in the 400–700 nm region.
The application of the TIP LAI and f APAR with our ET
models required some LAI and f APAR calibration. The TIP
LAI is a one-dimensional (1-D) equivalent LAI for solving
the radiative transfer in a three-dimensional (3-D) medium,
and it is consistent with the fluxes from which it is derived.
It is not consistent with LAI derived using a 3-D radiative
scheme that allows some form of horizontal canopy clumping (e.g., the MODIS MOD15A2 LAI product). In practical terms, this means that if an ET model was constructed
to use a MODIS-like LAI and f APAR, a straight use of the
project LAI and f APAR will result in the ET model producing lower values than expected for those biomes where horizontal clumping is significant (e.g., for forests). While the ET
dynamics may not be highly affected (there is a high degree
of correlation between different LAI and f APAR estimates),
the absolute values would be. As the SEBS, PM-MOD and
PT-JPL models have typically been used with the MODIS
Hydrol. Earth Syst. Sci., 20, 803–822, 2016
vegetation product, a rescaling of our TIP-derived LAI and
f APAR products against the MODIS product has been undertaken. For running the models on the tower scale, a local rescaling is conducted by a linear regression between the
MOD15A2 and the TIP values co-registered at each tower.
For global model simulations, individual rescaling per biome
or climate classification is conducted. For PT-JPL, given the
model internal relationships between these variables and the
vegetation indexes used as model inputs (see Table 1 in
Fisher et al., 2008), it can be discussed whether the original TIP LAI and f APAR or the rescaled LAI and f APAR
are the most appropriate to be used as model inputs. For simplicity we will apply also the rescaled LAI and f APAR, but
this choice will be further evaluated in future applications of
the model with the TIP LAI and f APAR inputs.
Figure 1 illustrates an example of both products at two
towers. The station Quebec – Eastern Boreal, Mature Black
Spruce (CA-Qfo) is located in an evergreen needleleaf forest and shows that the MOD15A2 and WACMOS-ET LAI
and f APAR absolute values differ considerably. This is
expected, as allowing some form of horizontal clumping
(MODIS 3-D radiative transfer scheme) or not (TIP 1-D)
can result in large differences in the estimated LAI and
f APAR in forests. It can be seen that the local calibration of the MODIS-like product retains the dynamics of the
WACMOS-ET product, while adding absolute values close
to the MODIS product. The station Brookings (US-Bkg) is
situated in a cropland area, where the effects of clumping are
much less severe, and the different LAI and f APAR values
are much closer.
2.2.5
Vegetation height
Vegetation height on the global scale is required by SEBS.
For shrubland and forest biomes the product developed by
Simard et al. (2011) was used as static canopy height cover.
For grassland and cropland biomes, where the temporal dynamics of canopy height can be more considerable, we approximated canopy height with the method by Chen et al.
(2015), with the minimum and maximum canopy height obtained from the static vegetation table of the North American
Land Data Assimilation System (NLDAS).
2.2.6
Soil moisture and vegetation optical depth
A soil moisture product combining observations from active
and passive microwave sensors has been developed as part of
the ESA Climate Change Initiative (CCI) and is adopted here
to provide the surface soil moisture data that are assimilated
into GLEAM. Details on the product algorithm and evaluation can be found in Liu et al. (2011b). The data are provided
on a regular grid with a resolution of 0.25◦ × 0.25◦ . The
product performs well in moderately vegetated regions but
shows higher uncertainties in densely vegetated regions (as
vegetation attenuates the microwave signal from the ground)
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D. Michel et al.: The WACMOS-ET project – Part 1
809
Figure 1. Time series for 2005–2006 of MODIS MOD15A2 LAI and f APAR, WACMOS-ET LAI and f APAR, and the MODIS-like LAI
and f APAR (referred to as “scaled” in the figures) at the tower stations CA-Qfo (top panels) and US-Bkg (bottom panels).
and mountainous areas (due to the high surface roughness)
(Liu et al., 2011b).
The retrieval of soil moisture from passive sensors discussed in Sect. 2.2.6 can be accompanied by an estimation
of the vegetation optical depth (VOD). VOD can be used to
account for the development of vegetation over the year as it
is a good proxy of vegetation water content (Liu et al., 2015).
Although most ET models traditionally use parameters derived from visible and near-infrared wavelengths, microwave
VOD is used by GLEAM. Here the long-term record by Liu
et al. (2011a) based on the application of the Land Parameter
Retrieval Model by Owe et al. (2001) is used by GLEAM.
2.2.7
ter equivalent estimates come from ESA GlobSnow. Since
GlobSnow covers the Northern Hemisphere only, data from
the National Snow and Ice Data Center (NSIDC) are used
in snow-covered regions of the Southern Hemisphere (Kelly
et al., 2003). The product combines satellite passive microwave measurements with ground-based weather station
data in a data assimilation scheme (Luojus and Pulliainen,
2010). The products exist at a daily resolution and a spatial
resolution of 25 km.
2.3
2.3.1
Tower data
Tower selection
Precipitation and snow
Observations of precipitation and snow water equivalent are
also required by GLEAM only. Precipitation is used both to
estimate the effects of soil water limitations on ET and to
calculate interception loss. To run the model on the tower
scale we use the Climate Prediction Center (CPC) Morphing Technique (CMORPH) (Joyce et al., 2004). CMORPH
transports the features of precipitation estimates derived from
low orbiter satellite microwave observations using information from geostationary satellite infrared (IR) data. Precipitation estimates are available every 30 min on a grid with
a spacing of 8 km at the Equator, although the resolution
of the individual satellite-derived estimates is coarser at
∼ 12 km × 15 km. The spatial coverage ranges from 60◦ N–
60◦ S. To run the model globally, we use the National Centers
for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) land precipitation estimates (Coccia and Wood, 2015). These precipitation estimates come
from the hourly CFSR output (Saha et al., 2010) but are
corrected using the observation-based data sets of the CPC
(Xie and Arkin, 1997) and the Global Precipitation Climatology Project (GPCP) (Adler et al., 2003). Finally, snow wawww.hydrol-earth-syst-sci.net/20/803/2016/
Model simulations are evaluated by comparison with the turbulent latent fluxes measured by the eddy-covariance technique at a selection of tower sites from FLUXNET (Baldocchi et al., 2001). A first sample of towers was compiled by
selecting those stations from the FLUXNET La Thuile synthesis data set which contain latent flux measurements in the
2005–2007 period, as well as the meteorological and radiation inputs required to run the ET models at the towers’
locations. The 24 selected stations are described in Table 2
and their geographical location is displayed in Fig. 2. While
some meteorological variables such as near-surface air temperature or humidity are measured at nearly all towers, other
inputs such as the surface net radiation or the ground heat
flux are measured at only a few towers. Some stations that
were very close to the shore or in places with regular flooding
were discarded. The final selection of 24 towers represents a
significant number of biomes and a reasonable sample of dry
and wet climate regimes.
In a later step, by removing the constraint of requiring local measurements of all the model inputs, the first selection
of 24 towers is extended to a total of 85 stations. This second selection is used to evaluate model performance when
Hydrol. Earth Syst. Sci., 20, 803–822, 2016
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Table 2. Stations selected to run the models with tower inputs. From left to right: station name; longitude; latitude; Köppen–Geiger Climate
Classification (KGCC); International Geosphere–Biosphere International Programme (IGBP) land cover; total number of days with data, no
precipitation number of days with data; evaporative fraction (EF) for the DJF, MAM, JJA, SON 3-month periods.
Name
Long
Lat
KGCC
IGBP
Days
AU-How
CA-Ojp
CA-Qfo
DE-Geb
DE-Har
DE-Kli
DE-Meh
DE-Wet
IT-MBo
IT-Noe
NL-Ca1
PT-Mi2
RU-Fyo
US-ARM
US-Aud
US-Bkg
US-Bo2
US-FPe
US-Goo
US-MOz
US-SRM
US-WCr
US-Wkg
US-Wrc
131.15◦ E
104.69◦ W
74.34◦ W
10.91◦ E
7.60◦ E
13.52◦ E
10.66◦ E
11.46◦ E
11.08◦ E
8.15◦ E
4.93◦ E
8.02◦ W
32.92◦ E
97.49◦ W
110.51◦ W
96.84◦ W
88.29◦ W
105.1◦ W
89.87◦ W
92.2◦ W
110.87◦ W
90.08◦ W
109.94◦ W
121.95◦ W
12.49◦ S
53.92◦ N
49.69◦ N
51.1◦ N
47.93◦ N
50.89◦ N
51.28◦ N
50.45◦ N
46.03◦ N
40.6◦ N
51.97◦ N
38.48◦ N
56.46◦ N
36.61◦ N
31.59◦ N
44.35◦ N
40.01◦ N
48.31◦ N
34.25◦ N
38.74◦ N
31.82◦ N
45.81◦ N
31.74◦ N
45.82◦ N
Aw
Dfc
Dfc
Cfb
Cfb
Cfb
Cfb
Cfb
Dfb
Csa
Cfb
Csa
Dfb
Cfa
BSk
Dfa
Dfa
BSk
Cfa
Cfa
BSk
Dfb
BSk
Csb
SV
ENF
ENF
CRO
MF
CRO
CRO
ENF
MF
WSA
NVM
SV
MF
CRO
OSH
CRO
CRO
GRA
NVM
DBF
OSH
DBF
GRA
ENF
114, 100
126, 101
253, 166
188, 113
105, 88
275, 98
444, 269
384, 182
149, 126
182, 182
38, 22
275, 221
374, 216
159, 131
219, 219
174, 172
192, 192
184, 184
183, 179
252, 252
139, 137
338, 239
137, 137
146, 107
EF
0.7, 0.7, 0.5, 0.3
0.2, 0.1, 0.3, 0.5
0.1, 0.1, 0.4, 0.4
0.0, 0.4, 0.5, 0.7
1.0, 0.5, 0.5, 0.7
0.0, 0.5, 0.5, 0.0
0.0, 0.3, 0.5, 0.5
0.1, 0.3, 0.4, 0.6
0.0, 0.6, 0.7, 0.9
0.7, 0.3, 0.2, 0.3
1.0, 0.6, 0.6, 0.9
0.5, 0.4, 0.3, 0.4
0.0, 0.4, 0.5, 0.4
0.4, 0.5, 0.3, 0.3
0.5, 0.2, 0.3, 0.5
0.6, 0.7, 0.9, 1.0
0.3, 0.3, 0.6, 0.3
1.0, 0.4, 0.6, 0.4
0.7, 0.6, 0.5, 0.6
0.3, 0.4, 0.5, 0.5
0.2, 0.1, 0.3, 0.4
0.1, 0.2, 0.6, 0.4
0.2, 0.1, 0.2, 0.3
0.4, 0.3, 0.3, 0.5
KGCC abbreviations: Aw – tropical wet and dry; BSk – semiarid midlatitudes; Cfa – humid subtropical; Cfb –
marine, mild winter; Csa – interior Mediterranean; Csb – coastal Mediterranean; Dfa – humid continental hot
summer, wet all year; Dfb: humid continental mild summer, wet all year; Dfc – subarctic with cool summer, wet all
year. IGBP abbreviations: SV – savannas; ENF – evergreen needleleaf forests; CRO – croplands; MF – mixed
forests; WSA – woody savannas; OSH – open shrublands; GRA – grasslands; DBF – deciduous broadleaf forests;
NVM – natural vegetation mosaic.
the models are run with the satellite data used for the global
runs.
2.3.2
In situ surface energy balance
While, in principle, the surface energy balance should close
at the tower, this is rarely the case: a lack of closure in
the surface energy balance of about 10–30 % is commonly
found when comparing the EC measurements against the energy balance residual (ER) term, i.e., the difference between
net radiation and the sum of the sensible, latent and ground
fluxes (e.g., Foken et al., 2006). Consequently, throughout
the paper the model evaluation is discussed by comparing it
with both the EC measurements and the in situ ER estimates.
2.3.3
In situ LST
To run SEBS, the broadband longwave radiometer measurements need to be converted into LST estimates. This is done
by inverting the equation relating the upwelling spectral radiance measured by the radiometer and the LST. Broadband
emissivity is required, and it is estimated from the MODISHydrol. Earth Syst. Sci., 20, 803–822, 2016
based Global Infrared Land Surface Emissivity Database
(Seemann et al., 2008) operated by the Cooperative Institute
for Meteorological Satellite Studies (CIMSS). The estimates
are calculated by following the approach suggested by Wang
et al. (2005) using a linear combination of narrowband emissivities at 8.5, 11 and 12 µm.
2.3.4
In situ vegetation height
SEBS also requires vegetation height to derive the surface
roughness values. In most cases a mean annual value can be
obtained from the tower metadata, and this value is adopted
here as vegetation height at the tower. However, a clear limitation in this assumption is that it does not include dynamic changes in vegetation height over time. As discussed
in Sect. 2.2, the importance of neglecting the temporal variability in height is biome-dependent; for instance, in forests
the mean vegetation height is typically more constant than in,
e.g., croplands, where the changes derived from agricultural
practices can be large.
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Figure 2. Location of the 24 FLUXNET stations used for the main analysis of the study. They are located on three different continents,
encompassing nine different biomes, i.e., vegetation mosaic, croplands, mixed forests, deciduous forest, savanna, evergreen needleleaf forests,
grasslands, woody savanna and shrublands.
2.4
2.4.1
ET experiments
Evaluation times
The model performance is investigated on sub-daily and
daily timescales. The tower data are available at 0.5 h intervals and have been time-integrated to 3 hours in order to run
the ET models at that sub-daily resolution. The satellite data
have been time-matched to the 3-hourly or daily resolutions
from their native resolution in different ways (see below),
depending on the type of data and original resolution. The 3hourly inputs were then aggregated to daily values in order to
run the models with tower-based daily inputs. The tower data
record is not always time-continuous, as in some instances
there are gaps in the record. This is not a problem for the PMMOD, PT-JPL and SEBS models because the ET estimates
depends only on the instantaneous atmospheric or surface
state. When inputs to the models and/or ET for the evaluation are missing, those three models are not run. Conversely,
GLEAM requires continuous data records to update the soil
moisture state variable. To facilitate running GLEAM with
tower inputs, the tower measurements are gap-filled with the
corresponding pixel data (see Sect. 2.2). ET estimates from
these periods are removed after the runs, so, as before, only
the time steps where tower forcing data are available are used
for model evaluation.
The models are validated against the tower ET only under
dry (non-raining) conditions, as EC gas analyzers are not reliable during rain events due to disturbance of the infrared
www.hydrol-earth-syst-sci.net/20/803/2016/
signal by droplets on the sensor (Burba et al., 2010; Hirschi
et al., 2016). Therefore, any days with precipitation as indicated by the tower or satellite precipitation are removed
from the validation, and the interception component from
PM-MOD, PT-JPL and GLEAM is not considered in the validations.
2.4.2
Nighttime ET
Only PM-MOD and GLEAM specifically deal with nighttime evaporation. Nevertheless, nighttime values are required
from all models to integrate the 3-hourly ET estimates to
daily values. For SEBS and PT-JPL negative nighttime estimates are set to 0 to allow the daily integration for those
models. To separate day and night, daylight times are identified by calculating the solar zenith angle. Time intervals,
where the cosine of the zenith angle is larger than 0.2, are
kept as day values. This day and night separation may be less
accurate than using a solar downward radiation threshold, but
it allows a day–night flag for those stations without solar radiation measurements. The impact of setting ET from SEBS
and PT-JPL to 0, as these models cannot specifically simulate nighttime conditions, is addressed in Sects. 3.1 and 3.2,
where sub-daily periods, including daytime, are investigated.
2.4.3
ET production
The following ET estimates are generated to evaluate model
performance.
Hydrol. Earth Syst. Sci., 20, 803–822, 2016
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D. Michel et al.: The WACMOS-ET project – Part 1
– Tower-based ET: ET generated by the four models using
the 3-hourly or daily in situ data (surface radiation, LST,
air temperature, air humidity, and wind speed and precipitation), the scaled WACMOS-ET LAI and f APAR
and gridded soil moisture and VOD data. Note that
the 3-hourly tower-based ET estimates are also timeintegrated to daily values, so daily ET estimates exist
both from the runs with daily inputs and from the integration of the 3-hourly ET estimates.
– Original-resolution satellite-based ET: ET generated by
the four models using the 3-hourly or daily satellite
data (SRB surface radiation, ERA-Interim air temperature, humidity, wind speed, CMORPH precipitation,
ESA-CCI soil moisture, scaled WACMOS-ET LAI and
f APAR) at their original resolutions. In situ LST is
still used here in order to have the same number of
SEBS estimates as in the tower-based ET (cloudiness,
satellite overpass time and revisiting times would have
notably reduced the number of SEBS estimates if the
satellite LST had been used). As for the tower-based
ET, 3-hourly tower-based ET estimates are also timeintegrated to daily values.
– Common-grid satellite-based ET: ET generated by the
four models using the 3-hourly satellite data resampled
to a common grid. In contrast to the previous runs, the
satellite data are not applied at their original resolutions but after resampling them to the sinusoidal grid
at ∼ 25 km, adopted to produce the global model runs.
Note that the CMORPH precipitation is replaced by the
CFSR-Land product in order to have global coverage
and that the LST is based on the AATSR observations.
3
Results and discussion
Here we look at the model performance against the in situ
measurements, when the models are run with tower-based
and satellite inputs. This section is divided into the three
subsections, each of them dealing with one of the three
experiments introduced in the previous Sect. 2.4.3. First,
the 3-hourly and daily runs based on in situ forcing at
24 FLUXNET stations (see Table 2) are investigated. In the
second part we look at the model performance at the same
stations using 3-hourly and daily resolution satellite forcing. Finally, the ET estimates from the run using 3-hourly
common-grid satellite forcing are compared to the in situ
measurements at 85 FLUXNET stations.
3.1
Three-hourly and daily tower-based ET
The agreement of modeled evaporative fraction (EF) – defined here as λE/Rn , using modeled λE and the net radiation from the respective forcing – with the measured EF
(i.e., based on tower measurements of λE and Rn ) gives an
Hydrol. Earth Syst. Sci., 20, 803–822, 2016
indication of the algorithm skill to model evaporative stress.
Figure 3 (top panel) illustrates the agreement of modeled
evaporative fraction with in situ measurements (derived using both EC and ER measurements of evapotranspiration;
see Sect. 2.3.2), when models are run with tower inputs.
GLEAM generally ranges between the EC and ER measurements, even at dry stations in open shrubland (OSH), woody
savannas (WSA) and grassland (GRA) biomes, e.g., Sardinia/Arca di Noe (IT-Noe), Audubon Research Ranch (USAud), Santa Rita Mesquite (US-SRM) and Walnut Gulch
Kendall Grasslands (US-Wkg). Only in the evergreen needleleaf forests (ENF) does GLEAM exceed the range of in situ
measurements. PT-JPL mostly agrees with the reference as
well, although it presents positive biases at some dry sites,
like Wind River Crane (US-Wrc) and IT-Noe. PM-MOD underestimates EF for most stations (but it is very close to the
EC measurements at six stations), while SEBS is characterized by an overall overestimation (for six stations SEBS EF
is within the tower EC–ER range). In terms of the model performance per biome type, it can be stated that models generally perform the best in croplands (CRO) and deciduous
broadleaf forest (DBF); at least this is the case for PT-JPL,
PM-MOD and GLEAM. SEBS seems to perform better in
grassland and savanna biomes (SAV). It is, however, difficult to derive robust conclusions on the model performance
as a function of biome due to the low number of stations per
biome type.
As the surface meteorology plays an important role in the
ET production, we also compare the point-scale model performance with the gridded ERA-Interim ET data set (ERA)
in Fig. 3 (top panel). ERA-Interim estimates are mostly
within the range of EF measurements. The good agreement
between ERA EF and the in situ measurements indicates that
the ERA-Interim meteorology reliably captures the station
conditions. It can also be stated that the point-scale towerforced EF derived with PT-JPL and GLEAM match the ERAInterim product based on a ∼ 75 km resolution.
A statistical assessment of the model performance is given
in Fig. 4, which shows the correlation (R 2 ), the RMSD and
the average of the bias normalized by the reference (MBD)
between modeled ET and tower measurement of ET (i.e., using the EC approach). In the left column of Fig. 4 the station
averages of the statistical inferences are shown according to
measured EF, i.e., from wet to dry. In general, the correlation
to in situ data is high in wet and in moderately wet biomes
for most sites and for all models. This is also true for SEBS,
despite its substantial overestimation of EF (see Fig. 3).
However, there seems to be a distinct decrease in R 2 from
wet to dry biomes for all models; this decrease in performance is lower for GLEAM and higher for PM-MOD, which
presents correlations (R 2 < 0.4) at dry sites. PT-JPL stands
out amongst the ensembles with the highest correlation at
most sites and especially in dry conditions. In comparison to
the mostly underestimated evaporative fraction derived with
PM-MOD (see Fig. 3), the RMSD of PM-MOD ET correwww.hydrol-earth-syst-sci.net/20/803/2016/
PT−JPL
PM−MOD
SEBS
GLEAM
Tower
US−SRM
US−Aud
IT−Noe
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ERA
Dry
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WSA
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Dry
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CRO
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DE−Meh
DE−Geb
DE−Kli
US−Bkg
RU−Fyo
DE−Har
IT−MBo
813
1.2
NL−Ca1
US−Goo
D. Michel et al.: The WACMOS-ET project – Part 1
Figure 3. Station means of 3-hourly EC-observed and tower-forced (top panel) and satellite-forced (bottom panel) evaporative fraction
against tower reference, as function of biomes, sorted from wet to dry (based on the biome average). The grey area denotes the range of
evaporative fraction between EC and ER measurements. The black line denotes EF derived from ERA-Interim ET (ERA) and Rn .
sponds to PT-JPL and GLEAM and even produces the lowest maximum value (0.13 mm h−1 ), followed by GLEAM
(0.17 mm h−1 ). Note that the large positive MBD values of
PT-JPL and SEBS (> 200 %) may partly result from forcing
ET to 0 during nighttime (see Sect. 2.4.2), when tower ET
is negative, and thus leading to large relative errors, even for
small negative reference ET values.
In order to evaluate the impact of using EC measurements
as reference (in contrast to the ER method in Fig. 4), Table 3 shows the overall average 3-hourly model performance
(i.e., the average of all station statistics) using both EC and
ER data as reference. Overall, the average statistics of PTJPL and GLEAM appear more favorable than those of SEBS
and PM-Mu, although the RMSD and MBD of PM-MOD
and the R 2 of SEBS are in general comparable to those
of GLEAM and PT-JPL. This is again to a large extent affected by the overall overestimation and underestimation by
SEBS and PM-MOD, respectively. The RMSD of SEBS is
significantly smaller when using the ER method as reference
(0.10 mm h−1 ) as opposed to using EC (0.13 mm h−1 ); on the
other hand, the RMSD of PM-MOD is larger compared to
ER (0.12 mm h−1 ) than compared to EC (from 0.06 mm h−1 ).
Note that the transpiration resistances in PM-MOD are calibrated based on a biome-dependent annual ET derived from
EC observations, which may explain the smaller RMSD and
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MBD when using EC as a reference. Finally, the RMSD station averages are similar to both in situ references for by PTJPL (0.08, 0.09 mm h−1 ) and GLEAM (0.08, 0.08 mm h−1 ).
Here the skill of models at representing ET at specific
times of the day is examined. Note that small nighttime ET
values from models and measurements may produce small
absolute errors and thus can improve the overall full-day
model performance in comparison to daytime periods, even
if the relative bias is large.
The Taylor diagrams in Fig. 5 show that correlations to
in situ observations (using the EC method) considering the
entire daily cycle (left panel) are very similar compared to
those considering daytime values only (left panel, top row) or
nighttime values only (right panel, top row). The overall R 2
with tower forcing including all models is 0.67 for full-day
as well as daytime evaluation and 0.68 for nighttime evaluation; this indicates that the results are independent of the
timescale. Note that nighttime is identified as cases when the
cosine of the zenith angle is < 0.2.
We can see (right panel, top row) that forcing negative
nighttime ET values of PT-JPL and SEBS to 0 (in contrast to
specific negative ET produced by PM-MOD and GLEAM;
see Sect. 2.4.2) does not have a substantial impact on the
overall agreement with tower measurements. However, it
should be noted that the uncertainty of nighttime EC mea-
Hydrol. Earth Syst. Sci., 20, 803–822, 2016
1.0
0.4
0.6
λE Rn
0.2
0.0
1.0
0.8
0.4
0.6
λE Rn
SEBS
GLEAM
0.2
0.3
PT−JPL
PM−MOD
1.0
0.8
300
0.4
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200
0.2
0.0
0
US−Bkg
IT−MBo
NL−Ca1
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AU−How
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DE−Meh
US−WCr
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US−Wrc
IT−Noe
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CA−Ojp
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US−Wkg
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−100
100
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400
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DE−Wet
US−FPe
PT−Mi2
US−Wrc
IT−Noe
US−Aud
CA−Ojp
US−SRM
US−Wkg
CA−Qfo
US−Bkg
IT−MBo
NL−Ca1
US−Goo
DE−Kli
DE−Geb
AU−How
US−Bo2
DE−Meh
US−WCr
DE−Har
US−MOz
RU−Fyo
US−ARM
DE−Wet
US−FPe
PT−Mi2
US−Wrc
IT−Noe
US−Aud
CA−Ojp
US−SRM
US−Wkg
CA−Qfo
0.0
0.2
0.1
0.2
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0.0
0.2
0.0
400
300
200
MBD [%]
100
0
−100
0.8
1.0
0.8
0.6
0.4
0.0
US−Bkg
IT−MBo
NL−Ca1
US−Goo
DE−Kli
DE−Geb
AU−How
US−Bo2
DE−Meh
US−WCr
DE−Har
US−MOz
RU−Fyo
US−ARM
DE−Wet
US−FPe
PT−Mi2
US−Wrc
IT−Noe
US−Aud
CA−Ojp
US−SRM
US−Wkg
CA−Qfo
0.4
0.2
0.8
0.4
0.6
λE Rn
0.0
1.0
0.2
SEBS
GLEAM
0.8
PT−JPL
PM−MOD
0.4
0.6
λE Rn
tower EF
0.1
RMSD [mm/h]
0.3
0.4
US−Bkg
IT−MBo
NL−Ca1
US−Goo
DE−Kli
DE−Geb
AU−How
US−Bo2
DE−Meh
US−WCr
DE−Har
US−MOz
RU−Fyo
US−ARM
DE−Wet
US−FPe
PT−Mi2
US−Wrc
IT−Noe
US−Aud
CA−Ojp
US−SRM
US−Wkg
CA−Qfo
0.0
0.2
0.4
R2
0.6
0.8
1.0
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Figure 4. Station mean statistics of 3-hourly model data against EC reference. Left-column panels: tower-forced ET; right-column panels:
satellite-forced ET; top-row panels: R 2 correlation coefficient (left y axis); middle-row panels: mean bias deviation (MBD, left y axis);
bottom-row panels: root mean square difference (RMSD, left y axis). For all plots the evaporative fraction is given by the grey area (right
y axis).
surements is high because of low turbulence. Hence, large
nighttime errors can be present not only in the ET simulations but also in the EC data.
Sub-daily resolution is desirable in evaporation modeling,
as it allows investigation of the underlying land–atmospheric
interactions during the daily cycle of the planetary boundary layer. Given the short timescale of these interactions, one
may expect that models that are able to reproduce short-term
variability in ET would also be able to provide more reliable aggregates on daily timescales. Therefore, we investigate whether the model performance would benefit from
solving evaporation at a 3-hourly resolution and aggregating
it to daily values, as opposed to generating the estimates with
daily input directly. Figure 5 (bottom row) clearly shows that
not much more skill is gained by producing daily ET based
on 3-hourly input (i.e., resolved diurnal cycles in the meteorological inputs) as opposed to forcing the models with the
original daily input; results are almost identical when using
Hydrol. Earth Syst. Sci., 20, 803–822, 2016
aggregated 3-hourly output (left panel, bottom row) or using
daily forcing (right panel, bottom row). In fact, for GLEAM
the correlation to the EC reference is slightly higher when
daily input is used, even if the standard deviation agrees
marginally less well with the reference.
Figure 6 shows the statistics of the models’ evaluation after forcing them with daily inputs. As expected, the general correlations become lower when daily (as opposed to
3-hourly) estimates are validated, since the daily cycle no
longer plays a role in the enhancement of correlations – this
was already highlighted by Table 3. Comparison of Figs. 4
and 6 shows that the decline in average R 2 from wet to dry
stations is less evident at a daily resolution. This may be due
to the smaller sample size when daily values are analyzed.
PM-MOD and SEBS in particular correlate poorly at dry stations (also at other stations, such as the moderately wet AUHow). PT-JPL and GLEAM perform worse (compared with
the 3-hourly resolution) at dry stations when they are run at
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Daytime
2.5
2.0
1.5
1.0
Standard deviation
0.5
0.0
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Standard deviation
0.5
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Forcing
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Satellite
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Forcing
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Satellite
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Daily forcing
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Model
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PM−MOD
SEBS
GLEAM
0.7
0.8
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●
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1.0
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Standard deviation
0.6
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Model
PT−JPL
PM−MOD
SEBS
GLEAM
0.1 0.2
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0.0
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n
tio
la
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re
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Aggregated daily
0.0
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Standard deviation
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Standard deviation
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or
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PM−MOD
SEBS
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PM−MOD
SEBS
GLEAM
Nighttime
0.1 0.2
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la
Full day
3-hourly
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or
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●
●
1.0
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Forcing
● Tower
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Satellite
1.5
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Figure 5. Taylor diagrams of 3-hourly model performance against EC reference in sub-daily periods (top-row panels) and as function of
temporal resolution (bottom-row panels). The left panel shows the average model statistics for full-day (compare to top row) and 3-hourly
output data (compare to bottom row). Daytime is defined as cases when the cosine of the sun elevation azimuth is > 0.2; nighttime is defined
as cases when the cosine of the sun elevation azimuth is < 0.2. Shown are the normalized standard deviation, the normalized RMSD and the
correlation coefficient (R).
Table 3. Summary of 24 stations average statistics for 3-hourly and
daily tower forcing. EC denotes the model agreement with the evapotranspiration reference from eddy-covariance measurements, and
ER is the model agreement with the evapotranspiration reference
based on the in situ energy residual. RMSD is given in millimeters
per hour for both 3-hourly data (3 h) and daily data (d).
R2
MBD
(%)
EC
ER
EC
RMSD
ER
EC
ER
PT-JPL
3h
d
0.77
0.61
0.78
0.60
0.08
0.04
0.09
0.05
53.1
47.8
37.4
21.8
PM-MOD
3h
d
0.58
0.43
0.55
0.41
0.06
0.04
0.12
0.06
−6.7
3.8
−18.2
−11.3
SEBS
3h
d
0.64
0.45
0.78
0.50
0.13
0.08
0.10
0.07
125.9
113.5
78.4
48.5
GLEAM
3h
d
0.70
0.60
0.80
0.66
0.08
0.03
0.08
0.04
31.9
15.6
−8.7
−14.1
daily resolutions. In terms of the RMSD and MBD, the results are quite similar to the 3-hourly findings, but in most
cases worst performance at the daily resolution is found at
dry stations. An exception is GLEAM, which shows smaller
RMSD at the dry stations when using daily rather than 3hourly resolution.
The change in overall MBD (against the EC reference)
from using 3-hourly tower input to using daily tower input is
from 53.1 to 47.8 % for PT-JPL, from −6.7 to 3.8 % for PMwww.hydrol-earth-syst-sci.net/20/803/2016/
MOD, from 125.9 to 113.5 % for SEBS, and from 31.9 to
15.6 % for GLEAM. While the pattern of EF (Fig. 3) and
MBD (Fig. 4) indicates a substantial underestimation of 3hourly ET by PM-MOD, this underestimation is attenuated
when daily input is used (−18.2 to −11.3 % compared to the
ER reference). Note that even if we employ the term daily
input, the PM-MOD model estimates day and night ET separately by using integrated day and night inputs (as opposed to
PT-JPL, SEBS and GLEAM, which use daily integrated inputs) and then combines them to provide a daily value. This
is how the PM-MOD model was originally used and how it
is implemented in this study for daily estimation. The better
agreement on a daily scale thus may reflect a more appropriate use of the inputs.
The similarity of the results for different temporal resolutions underlines the robustness of the modeling processes.
PT-JPL and GLEAM agree best with the in situ measurements, while SEBS yields a good correlation in comparison
to the other models yet produces the largest absolute errors
due to its large overestimation. PM-MOD produces the lowest correlation but agrees rather well in terms of absolute deviations.
Table 3 summarizes the main statistics of the model evaluation for the 3-hourly and daily tower inputs.
Hydrol. Earth Syst. Sci., 20, 803–822, 2016
1.0
0.4
0.6
λE Rn
0.2
0.0
1.0
0.8
0.4
0.6
λE Rn
SEBS
GLEAM
0.2
2
3
4
PT−JPL
PM−MOD
0.0
1
0.2
1.0
0.8
600
0.4
0.6
λE Rn
400
0.2
0.0
0
US−Bkg
NL−Ca1
IT−MBo
US−Goo
DE−Kli
DE−Geb
US−WCr
AU−How
DE−Meh
DE−Har
US−MOz
US−FPe
US−Bo2
DE−Wet
RU−Fyo
PT−Mi2
US−ARM
US−Wrc
US−Aud
IT−Noe
CA−Ojp
US−SRM
US−Wkg
CA−Qfo
−200
200
1.0
0.8
0.2
US−Bkg
NL−Ca1
IT−MBo
US−Goo
DE−Kli
DE−Geb
US−WCr
AU−How
DE−Meh
DE−Har
US−MOz
US−FPe
US−Bo2
DE−Wet
RU−Fyo
PT−Mi2
US−ARM
US−Wrc
US−Aud
IT−Noe
CA−Ojp
US−SRM
US−Wkg
CA−Qfo
0.0
0.4
0.6
λE Rn
800
US−Bkg
NL−Ca1
IT−MBo
US−Goo
DE−Kli
DE−Geb
US−WCr
AU−How
DE−Meh
DE−Har
US−MOz
US−FPe
US−Bo2
DE−Wet
RU−Fyo
PT−Mi2
US−ARM
US−Wrc
US−Aud
IT−Noe
CA−Ojp
US−SRM
US−Wkg
CA−Qfo
US−Bkg
NL−Ca1
IT−MBo
US−Goo
DE−Kli
DE−Geb
US−WCr
AU−How
DE−Meh
DE−Har
US−MOz
US−FPe
US−Bo2
DE−Wet
RU−Fyo
PT−Mi2
US−ARM
US−Wrc
US−Aud
IT−Noe
CA−Ojp
US−SRM
US−Wkg
CA−Qfo
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0
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800
600
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MBD [%]
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US−Goo
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US−WCr
AU−How
DE−Meh
DE−Har
US−MOz
US−FPe
US−Bo2
DE−Wet
RU−Fyo
PT−Mi2
US−ARM
US−Wrc
US−Aud
IT−Noe
CA−Ojp
US−SRM
US−Wkg
CA−Qfo
5
0.2
0.8
0.4
0.6
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0.0
1.0
0.2
SEBS
GLEAM
0.8
PT−JPL
PM−MOD
0.4
0.6
λE Rn
tower
1
RMSD [mm/d]
4
5
US−Bkg
NL−Ca1
IT−MBo
US−Goo
DE−Kli
DE−Geb
US−WCr
AU−How
DE−Meh
DE−Har
US−MOz
US−FPe
US−Bo2
DE−Wet
RU−Fyo
PT−Mi2
US−ARM
US−Wrc
US−Aud
IT−Noe
CA−Ojp
US−SRM
US−Wkg
CA−Qfo
0.0
0.2
0.4
R2
0.6
0.8
1.0
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Figure 6. Station mean statistics of daily data from daily input against EC reference. Left-column panels: tower-forced ET; right-column
panels: satellite-forced ET; top-row panels: R 2 correlation coefficient (left y axis); middle-row panels: mean bias deviation (MBD, left
y axis); bottom-row panels: root mean square difference (RMSD, left y axis). For all plots the evaporative fraction is given by the grey area
(right y axis).
3.2
Three-hourly and daily original-resolution satellite
ET
In this section we discuss the model performance using 3hourly and daily satellite forcing with original resolution at
the selected 24 FLUXNET stations. The findings are compared to the results of the tower forcing in the previous section in order to allocate model uncertainty to either the algorithms used or the common forcing.
The evaluation of 3-hourly modeled EF using satellite
forcing (Fig. 3, bottom panel) shows a very similar picture of
agreement with the reference compared to the results of the
tower forcing. Note that the satellite EF shown here slightly
differs from tower-forced EF, as the data availability of the
input time series may be different at some stations. The ET
overestimation by SEBS seems to be slightly emphasized
when using satellite input in comparison to the tower forcing. Note that the LST used in SEBS is still obtained from the
Hydrol. Earth Syst. Sci., 20, 803–822, 2016
tower measurements, as discussed in Sect. 2.4.1. EF derived
with PT-JPL and GLEAM still agrees well with the reference, yet GLEAM overestimates EF in dry biomes when using satellite forcing but is more accurate at needleleaf forest
sites. The good model performance of PT-JPL and GLEAM,
independent of forcing type, indicates a robust performance
of the models on the one hand and a reliable satellite forcing
– in the sense of their meteorology comparing well with the
in situ tower data – on the other hand.
In Fig. 3 (bottom panel) we also compare the model performance with the gridded ERA-Interim ET data set. Note
that while the tower forcing runs (top panel) are independent
of ERA-Interim, the satellite runs use ERA-Interim estimates
as inputs for the surface meteorology. As shown in Sect. 3.1,
the ERA-Interim EF product agrees with the in situ measurements. The correlation of the models to ERA-Interim is not
substantially improved with satellite input in comparison to
www.hydrol-earth-syst-sci.net/20/803/2016/
D. Michel et al.: The WACMOS-ET project – Part 1
the tower forcing, although the models now use the ERAInterim meteorology as input.
The station averages of the statistical indices R 2 , RMSD
and MBD of the models forced with satellite observations
(Fig. 4, bottom panel) against the in situ measurements underline the previously reported high similarity of modeled
ET based on tower and satellite forcing. Only the RMSD
of SEBS is slightly attenuated with remotely sensed forcing.
However, the algorithm is still characterized by substantial
overestimation.
In the following we compare the model performance with
daily satellite forcing to the model performance with daily
tower forcing. In accordance with the evaluation of 3-hourly
data (see Fig. 4), Fig. 6 indicates that the daily satellitebased ET products also correspond to the tower-based modeled ET. We want to highlight, however, that in contrast
to the 3-hourly runs, the RMSD of SEBS substantially increases when satellite input is used. This suggests that the
SEBS physical modeling captures the ET processes more accurately with the high temporal resolution inputs (3-hourly
vs. daily).
Table 4 provides a summary of the main statistics of the
model evaluation for the 3-hourly and daily satellite inputs.
3.3
Three-hourly common-grid satellite ET
Here the ET algorithms are tested against 85 FLUXNET stations using the gridded sinusoidal (∼ 25 km) satellite input
(as opposed to using their original input resolutions) in order
to evaluate the common-gridded global ET estimates on the
tower scale. Only the evaluation over the towers is discussed
here, with the evaluation on the global scale discussed in the
companion paper of Miralles et al. (2016). Note that the spatial mismatch between the tower fetch and the ∼ 750 km2 of
the gridded cells is very large, and the agreement between
the tower fluxes and the modeled ET certainly depends on
the tower conditions being representative of the corresponding gridded pixel. This was also the case for some of the
original-resolution satellite inputs used over the 24 stations,
such as the SRB radiation or the ERA-Interim meteorology.
The results of the satellite runs using common-grid forcing
are compared to the results using the tower and satellite inputs on the tower scale presented in Sects. 3.1 and 3.2.
The top panel of Fig. 7 shows the mean 3-hourly EF over
70 stations for PM-MOD, PT-JPL and GLEAM. For 15 of
the 85 stations the surface radiation or the ground flux was
not available; hence, the ER reference could not be calculated. As the gridded inputs use satellite LST from AATSR,
SEBS ET is only estimated at the midmorning AATSR overpass. The bottom panel of Fig. 7 shows the annual midmorning evaporative fraction, this time including SEBS. Due to
the 3-day revisiting time of AATSR and the lack of measurements in cloudy conditions, the number of available SEBS
ET estimates reduces drastically, compared with the previous simulations using tower LST. The bottom panel of Fig. 7
www.hydrol-earth-syst-sci.net/20/803/2016/
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Table 4. Summary of 24 station average statistics for 3-hourly and
daily satellite forcing. ERA denotes the agreement of ERA-Interim
evapotranspiration with the in situ reference evapotranspiration. For
other abbreviations, see Table 3. RMSD is given in millimeters per
hour for both 3-hourly data (3 h) daily data (d).
R2
MBD
(%)
EC
ER
EC
RMSD
ER
EC
ER
PT-JPL
3h
d
0.67
0.57
0.68
0.49
0.07
0.04
0.11
0.05
25.8
16.5
14.6
−1.7
PM-MOD
3h
d
0.47
0.35
0.47
0.29
0.07
0.04
0.14
0.06
−16.1
−17.9
−27.2
−34.2
SEBS
3h
d
0.59
0.42
0.71
0.41
0.13
0.09
0.11
0.08
145.1
160.2
148.6
123.9
GLEAM
3h
d
0.61
0.52
0.72
0.52
0.08
0.04
0.10
0.05
22.7
16.2
−4.6
−10.6
ERA
3h
d
0.51
0.62
0.45
0.50
0.10
0.07
0.14
0.07
111.3
114.7
87.0
74.6
shows station averages from all models only when SEBS ET
is available. Thus, it is based on fewer data and with the number of stations reduced to 67.
The 3-hourly model performances from PM-MOD, PTJPL and GLEAM correspond closely to the performance in
the analysis using the 24 towers and the original-resolution
satellite inputs. The EF station averages produced by PT-JPL
and GLEAM are very close at all locations and respond well
to the hydrological and energetic conditions expected in the
respective biome. The overall agreement with the range between EC and ER in situ measurements is comparable to
what has previously been found in the smaller sample of
stations (see Fig. 3). PM-MOD keeps underestimating ET,
except for the cropland biome, where the majority of station averages matches well with the reference. Concerning
the midmorning evaporative fractions, the PM-MOD, PT-JPL
and GLEAM patterns are all very similar to the case with
the full diurnal cycle. SEBS again tends to overestimate over
a large number of stations, compared with the in situ measurements. Overall, it can be stated that the model accuracy
and inter-model agreement obtained with in situ and satellite forcing on the tower scale could be reproduced with the
common-grid satellite forcing.
Figure 8 summarizes the results above by displaying standard deviation, correlation and RMSD of the modeled ET
shown in Fig. 7 against the EC reference. The Taylor plots
highlight the fact that the variability of PT-JPL, PM-MOD
and GLEAM is not substantially influenced by the low sample size for cases when SEBS ET is available. Again, the
similarity between Fig. 5 (left panel) for satellite forcing on
the tower scale and Fig. 8 for gridded input data confirms
the robustness of the analyses independent of tower and time
sampling.
Hydrol. Earth Syst. Sci., 20, 803–822, 2016
D. Michel et al.: The WACMOS-ET project – Part 1
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GLEAM
PT−JPL
PM−MOD
SEBS
GLEAM
Tower
ERA
Tower
ERA
Dry
0.4
0.6
PT−JPL
PM−MOD
OSH
WL
ENF
GRA
CRO
MF
1.0
SAV
EBF
0.0
0.2
λE Rn
0.8
Wet
0.4
0.6
Dry
OSH
WL
ENF
GRA
CRO
MF
SAV
EBF
0.0
0.2
λE Rn
0.8
Wet
Figure 7. Top panel: station means of 3-hourly sinusoidal gridded satellite-forced evaporative fraction for full days (70 stations) against tower
reference, as function of biomes, sorted from wet to dry (based on the biome average). Bottom panel: same as top panel but for midmorning
only (from 09:00 to 13:00 LT; 67 stations). The grey area denotes the range of evaporative fraction between EC and ER measurements. The
black line denotes EF derived from ERA-Interim ET and Rn .
0.0 0.5 1.0 1.5 2.0 2.5
0.99
Standard deviation
0.95
0.6
0.7
R
0.9
1
Model
PT−JPL
PM−MOD
2 SEBS
GLEAM
0.5
n
tio
R
0.8
0.4
la
re
or
0.7
Midmorning
0.3
C
0.6
n
tio
Model
PT−JPL
PM−MOD
2 GLEAM
0.5
0.1 0.2
0.0 0.5 1.0 1.5 2.0 2.5
0.4
la
re
or
Standard deviation
Full day
0.3
C
0.0 0.5 1.0 1.5 2.0 2.5
0.1 0.2
0.8
0.9
1
0.95
0.99
0.0 0.5 1.0 1.5 2.0 2.5
Figure 8. Taylor diagram plots of sinusoidal gridded model data
against tower EC reference. Left panel: full-day 3-hourly data compared to 85 stations. Right panel: midmorning data (from 09:00 and
13:00 LT) compared to 82 stations. Shown are the normalized standard deviation, the normalized RMSD and the correlation coefficient (R).
4
Conclusions
In this first part of the WACMOS-ET study, the skill of the
PT-JPL, PM-MOD, SEBS and GLEAM ET algorithms has
been tested on the tower scale against in situ measurements
at 24 FLUXNET sites. The algorithms are forced using in
situ meteorological data from these towers, covering the period 2005–2007 on three continents and across nine different biomes, while ensuring spatial consistency between input
and reference data. Additionally, the models are run for the
same period with reanalysis and satellite forcing of varying
spatial resolutions, including ERA-Interim (surface meteoHydrol. Earth Syst. Sci., 20, 803–822, 2016
rology), SRB (radiation), AATSR (LST), GlobAlbedo (LAI
and f APAR), CMORPH (precipitation) and WACMOS-CCI
(soil moisture). The models were run with 3-hourly and daily
input to assess the robustness of their performance for subdaily and daily resolution.
Our analyses have shown that the four models’ performance is robust in terms of changes in forcing types and
temporal resolutions (i.e., the changes do not alter the model
behavior at the selected stations significantly). Against the
in situ 3-hourly energy residual estimates at the tower,
the tower-based model simulations are ranked (according
to station averages) as follows: GLEAM (0.80, 0.08), PTJPL (0.78, 0.09), SEBS (0.78, 0.10) and PM-MOD (0.55,
0.12). The first value in the brackets denotes R 2 and the second value denotes RMSD in millimeters per hour. Compared
to the eddy-covariance measurements, however, the station
averages of RMSD do not reflect the same outcome. Due
to more substantial overestimation at two stations each, the
RMSD of PT-JPL (0.77, 0.08) and GLEAM (0.70, 0.08) are
larger than that of PM-MOD (0.58, 0.06). However, correlations are consistently higher for GLEAM and PT-JPL. Thus,
over our selection of towers and the reference period (2005–
2007), we judge GLEAM and PT-JPL as the algorithms more
closely matching the in situ observations. At some stations,
PM-MOD and SEBS also agree well with the observations,
but in general the PM-MOD and SEBS performance is characterized by under- and overestimation, respectively.
For the satellite forcing, the RMSD between the models
and the reference yields very similar numbers as for tower
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D. Michel et al.: The WACMOS-ET project – Part 1
forcing. Correlations are closer but in most situations slightly
smaller for the satellite forcings. This can be the result of discrepancies between the spatial resolution of satellite observations and tower measurements, although different inputs
errors (in situ vs. satellite) may also play a role. This performance closeness between in situ and satellite-derived values can be an indication of the spatial representativeness of
the tower measurements (i.e., reasonable spatial homogeneity around the tower) and the consistency of the input data set
across forcing types. This is underlined by a comparison to
the ∼ 75 km resolved reanalysis ET product of ERA-Interim,
which agrees well with the modeled ET across the different
biomes.
Regarding the analysis over the 85 stations, a similar overall picture is obtained using the ∼ 25 km common-grid ET
prepared for the global runs. The evaluations of McCabe
et al. (2015) of a different selection of towers (45 stations),
over a more extended period (1997–2007) and with different
satellite forcings (LandFlux forcings) also results in an overall similar analysis, confirming the robustness of the model
performance evaluations.
Using daily input data reduces the RMSD of the models
with the tower measurements but results in slightly worse
correlations. This is due to the lower variability of daily values in contrast to 3-hourly data (variability accentuated by
the diurnal cycle). However, the consistency of the model
agreements with the reference with regard to 3-hourly and
daily ET estimates highlights the robustness of the integration method applied to the models. This is also underlined by
the good agreements of modeled daily ET from aggregated
3-hourly output data with modeled daily ET from daily input.
While GLEAM and PM-MOD can produce negative ET,
PT-JPL and SEBS cannot operate under these conditions
(mostly at nighttime when the flux of available energy reverses sign) and their negative values are forced to 0. This
does not have a large impact on their full-day performance,
since these values occur at night, when tower ET is negative and with generally low values. Only for the relative bias
is the effect significant, since the two models consequently
overestimate ET in these cases.
In terms of high and low temporal input resolution, it was
found that using 3-hourly input data does not significantly
increase the accuracy of the models for producing daily ET.
Hence, it is sufficient to use daily input to achieve a similar
result if the intended application of the ET product does not
demand a reproduction of the diurnal cycle.
The ET models generally perform best in wet biomes and
tend to overestimate values in dry biomes, where ET is constrained by water availability. Focusing on water stress in the
model development within the community would thus provide the opportunity to obtain more robust simulations of
surface fluxes for global-scale employment.
The conducted analyses based on in situ ET are useful to
evaluate model performance, but there are some clear limitations. Our requirements for tower selection resulted in a
www.hydrol-earth-syst-sci.net/20/803/2016/
819
somewhat limited number of stations, so it would be desirable to extend the evaluations to larger regions in order to
better cover different climate and biome conditions. Therefore, in the companion paper of Miralles et al. (2016) our
analyses are extended by looking at the global spatiotemporal variability of the modeled ET, the closure of regional water budgets, and the discrete estimation of land evaporation
components or sources (i.e., transpiration, interception loss
and direct soil evaporation).
Acknowledgements. This study was funded by the European Space
Agency (ESA) and conducted as part of the project WACMOS-ET
(Contract no. 4000106711/12/I-NB). D. G. Miralles acknowledges
the financial support from the Netherlands Organization for Scientific Research through grant 863.14.004 and the Belgian Science
Policy Office (BELSPO) in the framework of the STEREO III
programme, project SAT-EX (SR/00/306). M. F. McCabe and
A. Ershadi acknowledge the support of the King Abdullah University of Science and Technology. The SEBS team is acknowledged
for facilitating discussions concerning the implementation of
their model. This work used eddy-covariance data acquired by
the FLUXNET community and in particular by the following
networks: AmeriFlux (US Department of Energy, Biological and
Environmental Research, Terrestrial Carbon Program, DE-FG0204ER63917 and DE-FG02-04ER63911), AfriFlux, AsiaFlux,
CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux,
Fluxnet-Canada (supported by CFCAS, NSERC, BIOCAP, Environment Canada and NRCan), GreenGrass, KoFlux, LBA, NECC,
OzFlux, TCOS-Siberia and USCCC. Data and logistical support
for the station US-Wrc were provided by the US Forest Service
Pacific Northwest Research Station. All WACMOS-ET forcing
data and ET estimates are publicly available and can be requested
through the project website (http://wacmoset.estellus.eu).
Edited by: P. Gentine
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